public void ConvolutionWithIdentityKernel_OutputEqualsInput_3x3()
{
var kernel = new Kernel2D <short>(Kernels.Short.Identity3x3);
var convolution = new Convolution2D <short>(kernel, 1, 1);
convolution.Convolve(m_InputImage, m_IntermediateImage);
// TODO - iterate through, check padding ?
convolution.Dispose();
}
public void ConvolutionWithIdentityKernel_OutputEqualsInput_1x1()
{
var kernel = new Kernel2D <short>(Kernels.Short.Identity1x1);
var convolution = new Convolution2D <short>(kernel, 1, 0);
convolution.Convolve(m_InputImage, m_IntermediateImage);
m_InputImage.Buffer.AssertDeepEqual(m_IntermediateImage.Buffer);
convolution.Dispose();
}
public void ConvolutionWith_1x1Stride_1x1Pad(int width, int height,
float[,] kernelInput, float[] input, float[] expectedInput)
{
var kernel = new Kernel2D <float>(kernelInput, Allocator.Temp);
var convolution = new Convolution2D <float>(kernel, 1, 1);
var image = new Image <float>(input, width, height, Allocator.Temp);
var outWidth = width - convolution.Padding.x * 2;
var outHeight = height - convolution.Padding.y * 2;
var expected = new Image <float>(expectedInput, width, height, Allocator.Temp);
var output = new Image <float>(outWidth, outHeight, Allocator.Temp);
convolution.Convolve(image, output);
output.Print();
expected.AssertEqualWithin(output);
convolution.Dispose();
image.Dispose();
output.Dispose();
expected.Dispose();
}
public void Execute()
{
Convolution.Convolve(Input, Output);
}